Fastify OpenTelemetry: Logging, Metrics, and Tracing in Practice

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Logging in Fastify: Foundations and Advanced Use

Logging is foundational for visibility in production. Fastify is built atop Pino, a super-fast, JSON-first logger. This section covers enabling and configuring Fastify’s logger, enriching logs with OpenTelemetry context, and integrating logs with your observability stack.

<details><summary><strong>Read detailed logging setup, enrichment, and production patterns (click to expand):</strong></summary>

• Start with basic logging configuration using Pino
• Enrich logs with OpenTelemetry span and trace context for correlation
• Route logs to platforms like Prometheus, Loki, Datadog, with code setup
• Rotation, redaction, and production tips
• Full details and code samples here →

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Instrumenting Fastify with OpenTelemetry for Tracing

Distributed tracing lets you follow a request through the entire stack. Fastify can be instrumented with OpenTelemetry for powerful, low-friction tracing. This section covers:

<details><summary><strong>Tracing installation, configuration, and advanced usage (click to expand):</strong></summary>

• Understanding OpenTelemetry and tracing basics
• Setting up the OpenTelemetry SDK and exporters (Jaeger, Zipkin, Tempo, OTLP)
• Instrumenting Fastify with the @autotelic/fastify-opentelemetry plugin
• Manual and automatic span management with code
• Troubleshooting context propagation and misconfigurations
• All step-by-step tracing integration examples →

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Metrics with Fastify and OpenTelemetry

Metrics power dashboards, SLOs, and pro-active alerts. Learn how to instrument Fastify using both the fastify-metrics plugin (for Prometheus) and OpenTelemetry’s metrics SDK for custom application-level insights.

<details><summary><strong>Metrics setup, custom business metric examples, and visualization (click for details):</strong></summary>

• Enable Prometheus HTTP metrics using fastify-metrics
• Configure scraping in Prometheus and visualize in Grafana
• Add custom app metrics (counters, histograms) with OpenTelemetry SDK
• Handle labels, cardinality, and aggregation for performance
• All metrics code samples and best practices →

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Exporters and Backend Integration (Jaeger, Prometheus, Zipkin, Sentry, etc.)

Exporters are how you move telemetry data into analysis backends. This section covers practical, backend-specific wiring for Jaeger, Zipkin, Prometheus, Tempo, Datadog, and Sentry.

<details><summary><strong>Automatic and advanced exporter configurations (full code and YAML) (expand for samples):</strong></summary>

• Installation and setup of Jaeger, Zipkin, and OTLP exporters
• Docker and backend-specific guidance for local and cloud setups
• Metrics endpoint configuration and scrape setup for Prometheus
• Handling exporter reliability, batching, security, and troubleshooting
• View example-rich exporter setups for all major backends →

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Real-World Case Studies and Troubleshooting

Move beyond theory with a complete deployment scenario and troubleshooting reference. This section gives you actionable troubleshooting workflows and highlights how a SaaS company successfully integrated Fastify and OpenTelemetry in production.

<details><summary><strong>Case study and hands-on troubleshooting playbooks (expand for workflow):</strong></summary>

• Step-by-step real deployment: Fastify, OpenTelemetry, Prometheus, Tempo, Grafana
• Actionable troubleshooting for missing telemetry, trace-log correlation, and exporter issues
• Operational checklists and version compatibility
• Follow the detailed case study and solution guides →

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Conclusion & Further Resources

Wrap-up, actionable setup checklist, and carefully selected official resources are provided to help users complete and refine their observability pipeline.

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Full Content Below

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1. Logging in Fastify: Foundations and Advanced Use

Full content, setup, and code—enabling, configuring, and enriching logs with context is in the previous messages. See above or here in this document.

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2. Instrumenting Fastify with OpenTelemetry for Tracing

Complete details and code for OpenTelemetry instrumentation, plugin setup, and troubleshooting are in the prior answer. See also the plugin’s README.

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3. Metrics with Fastify and OpenTelemetry

Metrics plugin and OTel SDK setup, code samples, and visualization examples are fully presented above. For custom metrics documentation see OpenTelemetry JS.

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4. Exporters and Backend Integration (Jaeger, Prometheus, Zipkin, Sentry, etc.)

See above for exporter code, Docker examples, Prometheus scraping, SaaS target guidance, and error handling tips. For exporter references, see:

• Jaeger Exporter
• OTLP Exporter
• Prometheus Exporter
• Sentry Integration

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5. Real-World Case Studies and Troubleshooting

A real Fastify deployment, troubleshooting playbooks, and diagnostic workflows are detailed above. Also see official guidance:

• OpenTelemetry Troubleshooting
• Fastify Metrics Troubleshooting
• Grafana Tempo Docs

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6. Conclusion & Further Resources

To fully enable Fastify observability with OpenTelemetry, follow this checklist:

1. Early SDK Init: Require/setup OpenTelemetry and exporters before your Fastify app and plugins.
2. Register Plugins: Use @autotelic/fastify-opentelemetry for tracing; fastify-metrics or OTel metrics for monitoring.
3. Exporter Config: Set and verify endpoints for Jaeger, Zipkin, OTLP, Prometheus, etc.
4. Log Enrichment: Attach trace fields in logs using Fastify hooks and OTel context.
5. Security: Restrict /metrics, /health, and telemetry endpoints to trusted networks. Use environment variables for API keys and config.
6. Verification: Check traces, metrics, and logs in backends before going live.
7. Troubleshooting: Use OTel debug logging and review app/collector logs regularly.
8. Routine Audit: Review dashboard SLOs, exporter versions, and integration periodically.

Key References:

• Fastify Docs
• OpenTelemetry JS
• Fastify Metrics
• OpenTelemetry Plugin Registry
• Jaeger Docs
• Prometheus
• Sentry OTel Integration